Infrastructure Engineering - Research Publications
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ItemSpatial Data Integration Challenges: Australian Case Studies(Spatial Sciences Institute, 2007)
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ItemBridging SDI design gaps(Centre of Geo-Information Technologies (cGIT), 2006)The environment we inhabit is integrated and to properly manage the environment it is necessary to look at all environmental components and making multi-criteria decision about environment mostly needs an integrated view of built and natural environmental components to better interpret it.Despite the integrated nature of environment and requirements of users to integrate different components of environment, information about different elements of environment is being collected and managed by fragmented agencies under different and mostly inconsistent policies and standards to satisfy their own needs –for a single discipline- with little attention to the broad range of users – a multi-disciplinary approach. This fragmentation results in heterogeneity of technical and non-technical issues surrounding integration of datasets.An SDI is an initiative to facilitate the cooperation among all stakeholders and the interaction with standards and technological components and one of its objectives is to facilitate the integration of multi-source spatial data sets.This paper aims to address different issues connected to the integration of multi-source data sets in order to better serve different communities through their SDI initiatives and also a better management and sharing of their spatial data. The paper aims to discuss both technical and non-technical issues related to the integration of multi-source data sets in alignment with an ongoing research project devoted to developing models, guidelines and associated tools to facilitate the integration of multi-source datasets within an SDI.
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ItemThe development of a frameworkand associated tools for the integration ofmulti-sourced spatial datasets( 2006)The world of today heavily relies on spatial data to manage the natural and built environments.Monitoring and controlling the impact of human activities on environment and the impact of naturalenvironment changes (including natural hazards) on built environment is a major policy driver formany jurisdictions. The mitigation of natural hazards including tsunami, earthquake and landslide andsecuring citizens against them has become another priority of nations , especially after Indonesia’stsunami. September 11 was also a remarkable point in history which attracts attentions towardsproviding more efficient counter-terrorism initiatives to secure citizens.In many countries meeting sustainable development objectives including social cohesion andenvironment protection together with economical growth is the most overwhelming policy driver. Inthese countries most of legislations and decisions are made, if they meet sustainable developmentobjectives.All above mentioned activities try to control the natural and built environments and monitor theimpacts of one environment on the other one. To control and monitor built and natural environments,the components of these environments need to be integrated to provide the factual model of realworld. Effective access and use of spatial data has been addressed by developing SDIs (Spatial DataInfrastructures) which one of its objectives is to address and provide requirements for effective dataintegration.The effective integration of built and natural environmental datasets is an ultimate goal of manyspatial decision making systems which has not been fully achieved, however technical integration andinteroperability of multi-sourced spatial data have received much attention. The integration of multisourcedspatial data due to the diversity of data providers needs more than technical tools andconsiderations. Institutional, social, legal and policy requirements must also be taken intoconsideration in order to achieve effective integratio
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ItemDevelopment of an interoperable tool to facilitate spatial data integration in the context of SDI(Taylor & Francis, 2010)The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature manipulation engines (FMEs), ontology-driven data integration and spatial mediators have been proposed. The major aim of these initiatives is to harmonize data sets and establish interoperability between different data sources. On the contrary, spatial data integration and interoperability is not a pure technical exercise, and there are other nontechnical issues including institutional, policy, legal and social issues involved. Spatial Data Infrastructure (SDI) framework aims to better address the technical and nontechnical issues and facilitate data integration. The SDIs aim to provide a holistic platform for users to interact with spatial data through technical and nontechnical tools. This article aims to discuss the complexity of the challenges associated with data integration and propose a tool that facilitates data harmonization through the assessment of multisource spatial data sets against many measures. The measures represent harmonization criteria and are defined based on the requirement of the respective jurisdiction. Information on technical and nontechnical characteristics of spatial data sets is extracted to form metadata and actual data. Then the tool evaluates the characteristics against measures and identifies the items of inconsistency. The tool also proposes available manipulation tools or guidelines to overcome inconsistencies among data sets. The tool can assist practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration.
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ItemSpatial data integration: a necessity for spatially enabling government(University of Melbourne, The Centre for SDIs and Land Administration, 2007)Introduction Most governmental decisions involve a spatial component (Williamson and Wallace, 2006), therefore informative decision making within governments is highly reliant upon multi-sourced spatial data. The ability to spatially enable governments through the use of integrated multi-source spatial data at different governmental levels makes governmental decisions incredibly efficient (Mitchell, 2006b), though governments rarely produce all the data required for their business. Rather, they obtain and integrate data from different sources. However, the diversity of data producers hinders effective spatial data integration. There are many technical and non-technical obstacles in the integration of multi-sourced spatial data and this is one of the major problems in sharing and using spatial data among government organizations. From a technical perspective, spatial data may differ semantically, syntactically and structurally. Institutional, social, policy and legal issues also hinder data integration. In order to effectively overcome these issues, a holistic framework is required to manage and address the issues. SDIs aim to facilitate the integration of multi-source spatial data by providing a holistic framework in which spatial data stakeholders (governments, private sector, etc) interact with spatial data effectively through technological components. There are inconsistencies in the various data within an SDI which lead to data inconsistency and hinder data integration. These inconsistencies should be managed through the SDI framework. However, at the moment, the SDI framework does not deal with these inconsistencies effectively. Hence, we need to identify and map the inconsistencies and develop tools and guidelines within the framework of an SDI to manage them. This will then make it easier for data to be integrated across and within government organisations.
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ItemSpatial Data Integrability and Interoperability in the Context of SDI(SPRINGER-VERLAG BERLIN, 2009-01-01)
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ItemGeo-web service tool for spatial data integrability( 2008)The integration of multi-source heterogeneous spatial data is one of the major challenges for many spatial data users. Users put much effort to identify and overcome inconsistency among data sets through a time-consuming and costly process. Spatial applications that rely on multi-source heterogeneous data also suffer from the lack of automatic mechanism to identify the inconsistency items and assign an appropriate solution for any particular item. An effective integration necessitates the identification of the inconsistency among data sets and the provision of necessary standards and guidelines in order to overcome the inconsistency, and then data sets can be manipulated based on the guidelines and proposed solutions.The paper follows two main streams. Firstly, the results of a number of case studies which have been conducted in order to identify the issues and challenges of spatial data integration are discussed. Then based on identified issues the design and development of a validation tool is discussed. The tool has been designed based on an approach which is presented in the paper. The tool aims to investigate multi-sourced spatial data and identify the items of inconsistency. The tool also proposes available guidelines to overcome the inconsistency. This tool can help practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration.
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ItemEnabling spatial data sharing through multi-source spatial data integration(GSDI, 2009)The dynamic environment of SDIs and the involvement of diverse spatial data providers present uncertainty for involving organizations. This pushes organizations to focus on cooperative data sharing relationships to deliver their objectives. Spatial data sharing provides transactions in which individuals, governments and businesses obtain access to spatial data and services from other stakeholders. However, spatial data sharing goes beyond simple data exchange and requires the provision of usable datasets. It is specifically important at multi-national level and Global SDI (GSDI). One of the most significant and demanding characteristics of usable datasets is the readiness of spatial datasets for integration with other datasets. However it is often difficult or even impossible for users to sensibly integrate datasets from different sources. This is because of the diversity of data standards, specifications and arrangements which have been utilized by organizations. Data providers adopt spatial data standards and specifications and establish data sharing arrangement based on their requirements which may differ form other organizations. Therefore, multi-source spatial datasets are associated with technical and non-technical inconsistency and heterogeneity. In order to facilitate the integration of multi-source spatial datasets, the investigation of the data integration process, potential barriers and challenges of spatial data integration and possible enablers and solutions is necessary. This paper aims to provide an investigation on the spatial data integration as a compelling reason for spatial data sharing. The investigation approach is based on a number of case studies. The case study investigation has also highlighted and identified a number of technical and non-technical barriers and issues of multi-source spatial data integration. The paper also capitalizes on the analysis of the case study investigation to identify the possible tools, solutions and enablers which can be utilized to facilitate the integration of multi-source datasets. In this regard, the paper presents a spatial data integration toolbox. The toolbox consists of a number of components including spatial data validation and integration tool, associated guidelines; and data integration metadata and data specification documents. The design and development of a spatial data validation and integration tool and associated guidelines have also been presented in the paper.